Xiang Fu
@xiangfu_ml
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research scientist at FAIR @AIatMeta/@OpenCatalyst. prev PhD @MIT_CSAIL, research intern @MSFTResearch
San Francisco, CA
Joined July 2019
One of the largest open dataset for materials quantum mechanical calculations and state-of-the-art ML potentials, open-sourced for both commercial and non-commercial use. We are eager to hear your feedback!
Introducing Meta’s Open Materials 2024 (OMat24) Dataset and Models! All under permissive open licenses for commercial and non-commercial use! Paper: Dataset: Models: 🧵1/x
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RT @abhshkdz: We're hiring for founding frontend, full-stack, and AI roles. Small, focused team shaping the future of digital assistants.…
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RT @ma_nanye: Inference-time scaling for LLMs drastically improves the model's ability in many perspectives, but what about diffusion model…
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RT @xie_tian: Excited to finally announce the publication of MatterGen on Nature. MatterGen represents a new paradigm of materials design w…
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RT @CorinWagen: New preprint! With @JosephJGair1 + the Gair lab @MSUChem, we built a set of strained conformers for benchmarking DFT functi…
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RT @RickyTQChen: Looking for strong candidates for a *postdoc* position with our team at FAIR NYC! We develop foundational methods for ge…
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RT @draykol: Our paper on predicting the emergence of crystals from amorphous precursors with deep learning potentials is now published in…
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RT @marceldotsci: new work! we follow up on the topic of testing which physical priors matter in practice. this time, it seems that predict…
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RT @balintmt: new preprint on solvation free energies: tl;dr: We define an interpolating density by its sampling process, and learn the co…
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@amelie_iska FermiNet aims to find wave-function Ansatz for solving the Schrödinger equation. This method aims to predict the charge density from DFT. FermiNet deals with more physical constraints, is more accurate, handles smaller system.
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RT @NandoDF: Let us please talk more about mental health in the AI community. I was shocked and reminded of this by the sad and tragic deat…
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RT @taylordsparks: I remember first hearing about @AIatMeta / @CarnegieMellon 's Open Catalyst Project back in 2020 or so. A truly huge DF…
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Presenting this work at NeurIPS tomorrow morning! I will be at NeurIPS from 12/11 to 12/14, let me know if you’d like to chat about AI for electronic structures, molecular dynamics, materials design, or the FAIR chemistry team!
Charge density is the core attribute of atomic systems in DFT. When representing and predicting charge density with ML, it is challenging to balance accuracy and efficiency. We propose a recipe that achieves SOTA on both: 1/5
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RT @ZongyiLiCaltech: #NeurIPS I am on the 2024-25 job market seeking faculty positions and postdocs! My goal is to advance AI for scientifi…
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RT @FrankNoeBerlin: Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equili…
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RT @MSFTResearch: Exploring synthetic DNA as a viable archival data storage medium required a range of expertise—both from within and outsi…
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RT @jehad__abed: Excited to unveil OCx24, a two-year effort with @UofT and @VSParticle! We've synthesized and tested in the lab hundreds of…
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RT @RichardSSutton: “Nature never appeals to intelligence until habit and instinct are useless. There is no intelligence where there is no…
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RT @GabriCorso: Thrilled to announce Boltz-1, the first open-source and commercially available model to achieve AlphaFold3-level accuracy o…
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RT @ask1729: 1/ What are key design principles for scaling neural network interatomic potentials? Our exploration leads us to top results o…
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RT @gklambauer: Does equivariance matter at scale? Should a model rather learn equi- and invariances from data or should the architecture…
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